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New study uses AI to help find downy mildew

Watermelon farmers, like all other farmers, seek ways to control diseases. The growers' needs is what drives Yiannis Ampatzidis to use artificial intelligence to detect pathogens early and accurately. 

One such disease, downy mildew, spreads like wildfire, said Ampatzidis, a UF/IFAS associate professor of agricultural and biological engineering.

For a new study, Ampatzidis used AI to help find downy mildew
In newly published research, Ampatzidis used spectral reflectance, the energy a surface reflects at specific wavelengths, of plant canopies and machine learning to quickly and efficiently detect downy mildew in several stages of the disease reports on

Ampatzidis and his research team successfully detected downy mildew in several stages of severity. "Our most important result was finding downy mildew in its earliest stage, which is critical to growers' ability to manage this disease," he said. 

Ampatzidis and his research team developed two methods, utilizing hyperspectral imaging and AI, one in the laboratory and the other using UAVs (drones) for field detection. 

For the next steps in his research, Ampatzidis wants to develop a simple and inexpensive drone-based sensor to improve the detection of downy mildew in watermelon plants.

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